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RAPID: The Changing Nature of "Calls" for Help with Hurricane Harvey: Comparing 9-1-1 and Social Media

RAPID：飓风“哈维”求助性质的变化：比较 9-1-1 和社交媒体

基本信息

批准号：
1760453
负责人：
Keri Stephens
金额：
$ 16.85万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2019-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1760453&HistoricalAwards=false
关键词：
RAPID Changing Nature Calls Help

项目摘要

RAPID: The Changing Nature of "Calls" for Help with Hurricane Harvey: 9-1-1 and Social MediaHurricane Harvey is the first big-data disaster where social media "calls" for help appear to have supplanted the overloaded 9-1-1 call systems; social media provided a visible, dialogic link to help. But this form of help-seeking behavior on public social media is relatively new. This project (1) captures the voices of hurricane victims and emergency response workers (both governmental and volunteer) (2) uses captured data to characterize the language present in actual social media calls for help, and (3) applies a big-data approach to a new emergency situation to assess that situation's calls for help. This project paves the way for new ways of thinking about how first-responders can utilize social media alongside traditional 9-1-1 when dispatching in future emergencies. The current practice in the crisis informatics literature is to mine social-media data during the disaster/aftermath around disaster-related keywords. However, such data collection pulls in everything--from solicitations for donations, to news stories--and it is challenging to filter signal from noise in such broad data sets. It is important to identify common threads in the language disaster victims use in their public "calls for help" to allow emergency managers to rapidly pinpoint these needs across varied communication channels and save lives. The approach in this project is unique because the combinatorial method isolates the signal of conversations by disaster victims on social media by understanding the specific keywords disaster victims use when requesting help. Using field interviews and surveys with Harvey and Irma victims, emergency response organizations, and organizations like the Texas/Cajun Navy--volunteer groups who organized their efforts through social media--the project will characterize what was posted, where calls for help were posted, and how these requests generated responses. The interview protocol will elicit examples of interviewees' social media posts to help develop ontologies of this content. In combination with historical data across several platforms (YouTube, Twitter, Reddit and Facebook) that will be purchased, the second phase of this project will match precise search queries (narrowed using boolean operators). The search mechanism will be driven by victims' social media behaviors and language specific to their experience of Harvey and Irma, rather than catchall hashtags and search terms. These types of victim-driven ontologies developed around specific experiences of a disaster are seriously lacking and understudied.

快速：哈维飓风是第一场大数据灾难，社交媒体“呼叫”帮助似乎已经取代了超载的9-1-1呼叫系统;社交媒体提供了一个可见的对话链接来帮助。但这种在公共社交媒体上寻求帮助的行为相对较新。该项目（1）捕捉飓风受害者和应急响应工作人员（包括政府和志愿者）的声音（2）使用捕获的数据来描述实际社交媒体求助中的语言，（3）将大数据方法应用于新的紧急情况，以评估这种情况下的求助。该项目为思考急救人员在未来紧急情况下如何利用社交媒体以及传统的9-1-1铺平了道路。危机信息学文献中目前的做法是围绕灾难相关的关键词挖掘灾难/灾后期间的社交媒体数据。然而，这样的数据收集涉及一切-从募捐到新闻故事-在如此广泛的数据集中过滤信号和噪音是具有挑战性的。重要的是要确定灾难受害者在公开“求助”中使用的语言中的共同点，以便应急管理人员能够通过各种沟通渠道迅速确定这些需求并拯救生命。该项目的方法是独一无二的，因为组合方法通过理解灾难受害者在请求帮助时使用的特定关键字，隔离了灾难受害者在社交媒体上的对话信号。通过对哈维和伊尔玛受害者，应急响应组织以及德克萨斯州/卡津海军等组织的实地采访和调查-通过社交媒体组织他们的努力的志愿者团体-该项目将描述发布的内容，发布的求助电话，以及这些请求如何产生响应。访谈协议将引出受访者的社交媒体帖子的示例，以帮助开发此内容的本体。结合将购买的多个平台（YouTube、Twitter、Reddit和Facebook）的历史数据，该项目的第二阶段将匹配精确的搜索查询（使用布尔运算符缩小范围）。搜索机制将由受害者的社交媒体行为和语言驱动，而不是笼统的标签和搜索词。这些类型的受害者驱动的本体论开发的具体经验的灾难是严重缺乏和研究不足。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Citizens Communicating Health Information: Urging Others in their Community to Seek Help During a Flood

公民传播健康信息：敦促社区中的其他人在洪水期间寻求帮助

DOI：
发表时间：
2018
期刊：
Proceedings of the ... International ISCRAM Conference
影响因子：
0
作者：
Stephens, K. K.;Li, J.;Robertson, B. W.;Smith, W. R.
通讯作者：
Smith, W. R.

Jumping in and Out of the Dirty Water… Learning from Stories while Doing Social Science

在脏水里跳进跳出……在做社会科学的同时从故事中学习

DOI：
10.1080/10410236.2019.1580995
发表时间：
2019
期刊：
Health Communication
影响因子：
3.9
作者：
Stephens, Keri K.
通讯作者：
Stephens, Keri K.

Evaluating the performance of Deep learning methods for hurricane-related image classification.

评估飓风相关图像分类的深度学习方法的性能。

DOI：
发表时间：
2019
期刊：
Proceedings of the ... International ISCRAM Conference
影响因子：
0
作者：
Johnson, M.;Murthy, D.;Robertson, B. W.;Smith, W. R.;& Stephens, K. K.
通讯作者：
& Stephens, K. K.

Social Media in Citizen-Led Disaster Response: Rescuer Roles, Coordination Challenges, and Untapped Potential

公民主导的救灾行动中的社交媒体：救援人员的角色、协调挑战和未开发的潜力

DOI：
发表时间：
2018
期刊：
International Conference on Information Systems for Crisis Response and Management
影响因子：
0
作者：
W. R. Smith;K. Stephens;Brett W. Robertson;Jing Li;D. Murthy
通讯作者：
D. Murthy

Using social media to call for help in Hurricane Harvey: Bonding emotion, culture, and community relationships